Application of adaptive neuro-fuzzy methodology for estimating building energy consumption

The huge demand for energy and construction materials has become an issue of great concern recently. The energy usage of buildings accounts for a large percentage of the total primary energy consumption. The total energy requirement of buildings is influenced by various factors, including environmental and climatic conditions, building envelope materials, insulation, etc. In this respect, estimating the operational energy of buildings is potentially helpful for architects and engineers in the early design and construction stages. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate the energy consumption of buildings according to the main building envelope parameters, namely material thickness and insulation K-value. Up to 180 simulations using different material thickness values and insulation properties are carried out in EnergyPlus software in order to use for estimation. This soft computing methodology is implemented with Matlab/Simulink and the performance is investigated.

[1]  R. Khalifehzadeh,et al.  Prediction of the effect of vacuum sintering conditions on porosity and hardness of porous NiTi shape memory alloy using ANFIS , 2007 .

[2]  William Perkins Spence.  Residential Framing: A Homebuilder's Construction Guide , 1993 .

[3]  L. Gustavsson,et al.  Life cycle primary energy analysis of residential buildings , 2010 .

[4]  Betul Bektas Ekici,et al.  Prediction of building energy consumption by using artificial neural networks , 2009, Adv. Eng. Softw..

[5]  Zhongya Zhang,et al.  Artificial neural networks applied to polymer composites: a review , 2003 .

[6]  Mirna Issa,et al.  Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties , 2012, Expert Syst. Appl..

[7]  T. N. Singh,et al.  Estimation of elastic constant of rocks using an ANFIS approach , 2012, Appl. Soft Comput..

[8]  Manuel R. Arahal,et al.  A prediction model based on neural networks for the energy consumption of a bioclimatic building , 2014 .

[9]  Vladan Babovic,et al.  GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING 1 , 2001 .

[10]  Peter F. Smith.  Architecture in a Climate of Change , 2001 .

[11]  D. Kolokotsa,et al.  Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal–visual comfort and indoor air quality satisfaction , 2001 .

[12]  Shahaboddin Shamshirband,et al.  Structure, energy and cost efficiency evaluation of three different lightweight construction systems used in low-rise residential buildings , 2014 .

[13]  Yu Sun,et al.  An ANFIS model for the prediction of flow stress of Ti600 alloy during hot deformation process , 2011 .

[14]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[15]  Ashok Kumar,et al.  Experimental evaluation of insulation materials for walls and roofs and their impact on indoor thermal comfort under composite climate , 2013 .

[16]  Hongye Su,et al.  Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system , 2010 .

[17]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[18]  Mirna Issa,et al.  Adaptive neuro fuzzy controller for adaptive compliant robotic gripper , 2012, Expert Syst. Appl..

[19]  Okyay Kaynak,et al.  Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles , 2007, Expert Syst. Appl..

[20]  Dalibor Petkovic,et al.  Adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability , 2011, Neural Computing and Applications.

[21]  Mohammad. Rasul,et al.  Recent Developments of Advanced Fuzzy Logic Controllers Used in Smart Buildings in Subtropical Climate , 2014 .

[22]  Manuel R. Arahal,et al.  Neural network and polynomial approximated thermal comfort models for HVAC systems , 2013 .

[23]  Shahaboddin Shamshirband,et al.  Adaptive neuro-fuzzy selection of the optimal parameters of protective spur dike , 2014, Natural Hazards.

[24]  Kamil Kaygusuz,et al.  Renewable energy and sustainable development in Turkey , 2002 .

[25]  Shahaboddin Shamshirband,et al.  Predicting optimum parameters of a protective spur dike using soft computing methodologies - A comparative study , 2014 .

[26]  B. Karaağaç,et al.  Predicting optimum cure time of rubber compounds by means of ANFIS , 2012 .

[27]  Sauro Longhi,et al.  Fuzzy logic home energy consumption modeling for residential photovoltaic plant sizing in the new Italian scenario , 2014 .

[28]  Serhat Burmaoglu,et al.  A fuzzy multi-criteria decision making approach to assess building energy performance , 2014 .

[29]  A.A.J.F. Van den Dobbelsteen,et al.  Sustainable bearing structures , 2002 .

[30]  Betul Bektas Ekici,et al.  Prediction of building energy needs in early stage of design by using ANFIS , 2011, Expert Syst. Appl..

[31]  Moncef Krarti,et al.  Genetic-algorithm based approach to optimize building envelope design for residential buildings , 2010 .

[32]  Wen-Shing Lee,et al.  Evaluating and ranking energy performance of office buildings using fuzzy measure and fuzzy integral , 2010 .

[33]  Ambrose Dodoo,et al.  Effect of thermal mass on life cycle primary energy balances of a concrete- and a wood-frame building , 2012 .

[34]  Edward Allen,et al.  Fundamentals of residential construction , 2002 .

[35]  S. E. Diaz,et al.  The use of earth–air heat exchanger and fuzzy logic control can reduce energy consumption and environmental concerns even more , 2013 .

[36]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[37]  Melih İnal,et al.  Determination of dielectric properties of insulator materials by means of ANFIS: A comparative study , 2008 .

[38]  R. H. Fouad,et al.  ELECTRICITY CONSUMPTION IN THE INDUSTRIAL SECTOR OF JORDAN: APPLICATION OF MULTIVARIATE LINEAR REGRESSION AND ADAPTIVE NEURO‐FUZZY TECHNIQUES , 2009 .

[39]  Luís Bragança,et al.  Sustainability assessment of an innovative lightweight building technology for partition walls – Comparison with conventional technologies , 2013 .

[40]  Aris Tsangrassoulis,et al.  On the energy consumption in residential buildings , 2002 .

[41]  Nadav Malin,et al.  Green Building Products: The GreenSpec Guide to Residential Building Materials , 2005 .

[42]  Shahaboddin Shamshirband,et al.  RETRACTED ARTICLE: Soft computing methodologies for estimation of energy consumption in buildings with different envelope parameters , 2016 .

[43]  Babak Rezaee,et al.  Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers , 2009, Expert Syst. Appl..

[44]  Hiroki Tamura,et al.  Batch type local search-based adaptive neuro-fuzzy inference system (ANFIS) with self-feedbacks for time-series prediction , 2009, Neurocomputing.

[45]  Jian Chu,et al.  Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A compa , 2011 .

[46]  Abdy Kermani.  Structural Timber Design , 1998 .

[47]  Vladan Babovic,et al.  Rainfall‐Runoff Modeling Based on Genetic Programming , 2006 .

[48]  Curtis Collins,et al.  Adaptive neuro-fuzzy control of a flexible manipulator , 2005 .